{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0cTM_BOrBUSU"
      },
      "source": [
        "# Merge multiple COCO annotation JSON files into one file. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cwJuts2DBaaU"
      },
      "source": [
        "Given multiple COCO annotated JSON files, your goal is to merge them into one COCO annotated JSON file. \n",
        "\n",
        "A merged COCO annotated JSON file is required where all the data is in one place and it becomes easy to split it into a training and validation JSON file according to the percentage ratio. In case you already have a validated COCO annotated JSON file, then this notebook can be used to merge multiple files into one training COCO annotated JSON file. \n",
        "\n",
        "This notebook uses a third party library to accomplish this task. Recursion is used to combine multiple JSON files using a third party library. \n",
        "\n",
        "This notebook is an end to end example. When you run the notebook, it will take all the multiple JSON files and will output one JSON file.  "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aDE5tjSUm1qu"
      },
      "source": [
        "**Note** - In this example, we assume that all our data is saved on Google drive and we will also write our outputs to Google drive. We also assume that the script will be used as a Google Colab notebook. But this can be changed according to the needs of users. They can modify this in case they are working on their local workstation, remote server or any other database. This colab notebook can be changed to a regular jupyter notebook running on a local machine according to the need of the users."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BM-tYHTlWhDQ"
      },
      "source": [
        "## **MUST DO** - Install the package and restart runtime"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zegGuOmQGBOr"
      },
      "outputs": [],
      "source": [
        "# install python object detection insights library to merge multiple COCO annotation files\n",
        "!pip install pyodi\n",
        "\n",
        "# RESTART THE RUNTIME in order to use this library"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l7eLOdQ5F33b"
      },
      "source": [
        "## Run the below command to connect to your google drive"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "h5soS-6URktT"
      },
      "outputs": [],
      "source": [
        "# import other libraries\n",
        "from google.colab import drive\n",
        "import pyodi\n",
        "import subprocess\n",
        "import sys\n",
        "import os\n",
        "import json\n",
        "import numpy as np\n",
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mTXqVbFdlqxi",
        "outputId": "b12566b2-458f-4673-eb97-7cf30075e258"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mounted at /content/gdrive\n",
            "Successful\n"
          ]
        }
      ],
      "source": [
        "# connect to google drive\n",
        "drive.mount('/content/gdrive')\n",
        "\n",
        "# making an alias for the root path\n",
        "try:\n",
        "  !ln -s /content/gdrive/My\\ Drive/ /mydrive\n",
        "  print('Successful')\n",
        "except Exception as e:\n",
        "  print(e)\n",
        "  print('Not successful')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U-80gvViT833"
      },
      "source": [
        "## Visualization function"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ipOuE69eT6Vk"
      },
      "outputs": [],
      "source": [
        "def data_creation(path: str) -\u003e pd.DataFrame:\n",
        "  \"\"\"Create a dataframe with the occurences of images and categories.\n",
        "  Args:\n",
        "    path: path to the annotated JSON file.\n",
        "  Returns:\n",
        "    dataset consisting of the counts of images and categories.\n",
        "  \"\"\"\n",
        "  # get annotation file data into a variable\n",
        "  with open(path) as json_file:\n",
        "    data = json.load(json_file)\n",
        "\n",
        "  # count the occurance of each category and an image in the annotation file\n",
        "  category_names = [i['name'] for i in data['categories']]\n",
        "  category_ids = [i['category_id'] for i in data['annotations']]\n",
        "  image_ids = [i['image_id'] for i in data['annotations']]\n",
        "\n",
        "  # create a dataframe\n",
        "  df = pd.DataFrame(\n",
        "      list(zip(category_ids, image_ids)), columns=['category_ids', 'image_ids'])\n",
        "  df = df.groupby('category_ids').agg(\n",
        "      object_count=('category_ids', 'count'),\n",
        "      image_count=('image_ids', 'nunique'))\n",
        "  df = df.reindex(range(1, len(data['categories']) + 1), fill_value=0)\n",
        "  df.index = category_names\n",
        "  return df\n",
        "\n",
        "def visualize_detailed_counts_horizontally(path: str) -\u003e None:\n",
        "  \"\"\"Plot a vertical bar graph showing the counts of images \u0026 categories.\n",
        "  Args:\n",
        "    path: path to the annotated JSON file.\n",
        "  \"\"\"\n",
        "  df = data_creation(path)\n",
        "  ax = df.plot(\n",
        "      kind='bar',\n",
        "      figsize=(40, 10),\n",
        "      xlabel='Categories',\n",
        "      ylabel='Counts',\n",
        "      width=0.8,\n",
        "      linewidth=1,\n",
        "      edgecolor='white')  # rot = 0 for horizontal labeling\n",
        "  for p in ax.patches:\n",
        "    ax.annotate(\n",
        "        text=np.round(p.get_height()),\n",
        "        xy=(p.get_x() + p.get_width() / 2., p.get_height()),\n",
        "        ha='center',\n",
        "        va='top',\n",
        "        xytext=(4, 14),\n",
        "        textcoords='offset points')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4hF5F_QE627R"
      },
      "source": [
        "## Define the paths of inputs and outputs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qUE6cHse3zOT"
      },
      "outputs": [],
      "source": [
        "def list_full_paths(directory):\n",
        "    '''return the files names in a directory with absolute path.\n",
        "    Args:\n",
        "      directory: path where all the files that need to merge are saved.\n",
        "    '''\n",
        "    return [os.path.join(directory, file) for file in os.listdir(directory)]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "a5UVAF--2nas"
      },
      "outputs": [],
      "source": [
        "folder_with_jsons = '/mydrive/TFHub/jsons/' #@param {type:\"string\"}\n",
        "output_merged_file = '/mydrive/TFHub/jsons/merged.json' #@param {type:\"string\"}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "80gZnQCU30gK",
        "outputId": "d3fdb152-c63a-42b5-f5bf-f4d50eb16d02"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "8"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# get a list of all the JSON files that need to merge with their absolute paths\n",
        "list_of_json_files = list_full_paths(folder_with_jsons)\n",
        "len(list_of_json_files)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6ot4VWOcSTWO"
      },
      "source": [
        "# Merge the files"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hngtw3K6S7Qx"
      },
      "outputs": [],
      "source": [
        "def merge_two_files(file1, file2, output_file):\n",
        "  \"\"\"Function to merge 2 files\n",
        "\n",
        "  Args:\n",
        "    file1: path of the 1st COCO annotation json file\n",
        "    file2: path of the 2nd COCO annotation json file\n",
        "    output_file: path of the output COCO annotation json file after merge\n",
        "\n",
        "  Returns:\n",
        "    Path of the merged COCO annotation json file\n",
        "  \"\"\"\n",
        "  subprocess.run(['pyodi', 'coco', 'merge', file1, file2, output_file])\n",
        "  return output_file\n",
        "\n",
        "def merge_multiple_files(list_of_files,output_file_path):\n",
        "  \"\"\"Recursive function to merge multiple files\n",
        "\n",
        "  Args:\n",
        "    list_of_files: list of all the COCO annotation json files that need to be merged \n",
        "    output_file_path: path of the output COCO annotation json file after merge\n",
        "\n",
        "  Returns:\n",
        "    Path of the merged COCO annotation json file\n",
        "  \"\"\"\n",
        "  if len(list_of_files) == 2:\n",
        "    return merge_two_files(list_of_files[0], list_of_files[1], output_file_path)\n",
        "\n",
        "  else:\n",
        "    return merge_two_files(list_of_files[0], merge_multiple_files(list_of_files[1:], output_file_path), output_file_path)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "__Wj0rO2D-HT"
      },
      "source": [
        "The output of the below code will be a merged COCO annotation file in the same directory."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZMKFOjchVMlH",
        "outputId": "815a83a3-43ae-478d-abaa-58181f034b94"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Total number of files to merge : 8\n",
            "Merge Done\n"
          ]
        }
      ],
      "source": [
        "# call function to merge multiple files\n",
        "print('Total number of files to merge :', len(list_of_json_files))\n",
        "merge_multiple_files(list_of_json_files, output_merged_file)\n",
        "\n",
        "print('Merge Done')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vf4ESHiDEQYF"
      },
      "source": [
        "## Visualize the results"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 429
        },
        "id": "UBwMf-W0EqG1",
        "outputId": "9bfbfb43-2ad2-4c18-8d75-05ea56a9573e"
      },
      "outputs": [
        {
          "data": {
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\n",
            "text/plain": [
              "\u003cFigure size 2880x720 with 1 Axes\u003e"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# visualize the merged COCO annotated JSON file\n",
        "visualize_detailed_counts_horizontally(output_merged_file)"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}
